Abstract
Species Distribution Models (SDMs) are widely used to understand environmental controls on species’ ranges and to forecast species range shifts in response to climatic changes. The quality of input data is crucial determinant of the model's accuracy. While museum records can be useful sources of presence data for many species, they do not always include accurate geographic coordinates. Therefore, actual locations must be verified through the process of georeferencing. We present a practical, standardized manual georeferencing method (the Spatial Analysis Georeferencing Accuracy (SAGA) protocol) to classify the spatial resolution of museum records specifically for building improved SDMs. We used the high‐elevation plant Saxifraga austromontana Wiegand (Saxifragaceae) as a case study to test the effect of using this protocol when developing an SDM. In MAXENT, we generated and compared SDMs using a comprehensive occurrence dataset that had undergone three different levels of georeferencing: (1) trained using all publicly available herbarium records of the species, minus outliers (2) trained using herbarium records claimed to be previously georeferenced, and (3) trained using herbarium records that we have manually georeferenced to a ≤ 1‐km resolution using the SAGA protocol. Model predictions of suitable habitat for S. austromontana differed greatly depending on georeferencing level. The SDMs fitted with presence locations georeferenced using SAGA outperformed all others. Differences among models were exacerbated for future distribution predictions. Under rapid climate change, accurately forecasting the response of species becomes increasingly important. Failure to georeference location data and cull inaccurate samples leads to erroneous model output, limiting the utility of spatial analyses. We present a simple, standardized georeferencing method to be adopted by curators, ecologists, and modelers to improve the geographic accuracy of museum records and SDM predictions.
Highlights
Climate change is predicted to result in massive species range shifts and population-level extinctions (Clark, Bell, Kwit, & Zhu, 2014; Hijmans & Graham, 2006; Thomas et al, 2004; Thuiller, Lavorel, Araújo, Sykes, & Prentice, 2005)
Understanding the present and future distributions of species is critical for applications in conservation, ecology, biogeography, phylogenetic analysis, phenology, landscape ecology, and beyond (Davis et al, 2015; Fois, Fenu, Lombraña, Cogoni, & Bacchetta, 2015; Forester et al, 2013; Lenoir, Gégout, Marquet, De Ruffray, & Brisse, 2008; Newbold, 2010)
Species Distribution Models (SDMs), especially those implemented in MAXENT, are the most common tools used to determine habitat suitability
Summary
Climate change is predicted to result in massive species range shifts and population-level extinctions (Clark, Bell, Kwit, & Zhu, 2014; Hijmans & Graham, 2006; Thomas et al, 2004; Thuiller, Lavorel, Araújo, Sykes, & Prentice, 2005). There are a variety of modeling methods and algorithms for generating SDMs, correlative models constructed using only species occurrence records and climate data are commonly used tools (Bucklin et al, 2015; Flower et al, 2013; Guillera-Arroita et al, 2015; Oke & Thompson, 2015). Sampling bias and imperfect detection are noted limitations of the current available data for species distributions (Boakes et al, 2010; Fourcade, Engler, Rödder, & Secondi, 2014; Guillera-Arroita et al, 2015; Newbold, 2010) Among all these potential sources of model uncertainty, one important variable for creating reliable SDMs is the accuracy of the species occurrence localities (Newbold, 2010). We focus on a single plant species, the methods could be extended to any taxon with historical museum or herbarium occurrence records
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